Hydroclimatic Risk Analysis in the Usumacinta River Basin

Hydroclimatic Risk Analysis in the Usumacinta River Basin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Hydroclimatic Risk Analysis in the Usumacinta River Basin Yaridalia Ramirez Abundis, Maritza Liliana Arganis Juárez, Ramon Domínguez Mora This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6579639/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This case study explores the impact of climate variability and change on the Usumacinta River, the most water-abundant river in Mexico. Climate change has intensified extreme hydrometeorological events, increasing the vulnerability of communities along the river. Historical climate data from meteorological stations, along with models (CNRMCM5, GFDL-CM3, HADGEM2-ES), were used to project future climate scenarios under RCP 4.5 and RCP 8.5. Results show a predicted increase in precipitation and temperature by 2040, with maximum temperatures rising by up to 5°C. Using the modified Svanidze method, the study forecasts a 28% increase in river flow compared to the highest recorded flood in 2008, posing significant risks to local populations and infrastructure. The findings highlight the need for resilient infrastructure, improved water management, and climate adaptation strategies to mitigate future flood risks. The Usumacinta River plays a crucial role in regional biodiversity and economic activities, making it essential to prioritize flood prevention and disaster preparedness. This research provides critical insights for strengthening the resilience of communities in the face of growing climate threats. Climate change Precipitation Svanidze Forecast flows flood Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights Signs of climate change Promote actions to reduce effects of climate change The change in rainfall patterns causes floods which alters the environment and makes cohabitation difficult. Forecast future floods with climate patterns. Identify the available rainfall for an integrated management of the water resource. 1 INTRODUCTION The first studies on climate modeling for Mexico were conducted at the UNAM Atmospheric Sciences Center, initially using the Climate Thermodynamics Model. Subsequently, a country study was developed, diagnosing the vulnerability of coastal areas (Caso-Chávez et al., 2022). The most relevant international studies for Mexico are the National Communications reported to the United Nations Framework Convention on Climate Change (CMNUCC); these reports have facilitated multidisciplinary studies on potential impacts in Mexico, describing current climate and future projections based on scenarios from the IPCC Fourth Assessment Report (SRES) and Fifth Assessment Report (RCP). One notable effort at the state level is the "State Climate Action Programs," which conduct regional analyses of climate change scenarios to comply with Mexico's General Climate Change Law, establishing that states must have such programs (Article 8). The National Climate Change System (SINACC) aims to foster synergies to address the country's vulnerability and risks and prioritize mitigation and adaptation actions to climate change (Salib, 2020). The North Atlantic Oscillation (NAO) is a periodic atmospheric phenomenon characterized by movements of large atmospheric air masses on an intercontinental scale, with a decisive impact on climatology. Its nature is like that of the El Niño-Southern Oscillation (ENSO), although much less known. The first official scientific description of both events is attributed to the same person, the English meteorologist Sir Gilbert Thomas Walter, who published it in an article in 1923. Despite the limited number of studies on the North Atlantic Oscillation (NAO), its crucial effect on climate variability in the regions it encompasses should not be underestimated. These regions include North America (Mexico, United States, and Canada) on the northern side of the Atlantic Ocean and Northwestern Europe on the opposite side of the continent. One of the factors that has brought attention to the topic of the North Atlantic Oscillation (NAO) is its likely relationship with the current atmospheric CO 2 concentration. Another factor is the possibility that its influence on global climate extends beyond the North Atlantic, due to its potential connection with El Niño event. Given that the Mexican Republic falls within the North Atlantic Oscillation's influence zone, study results have provided critical information demonstrating climate variation in Mexico. Therefore, short- and long-term rainfall forecasting is crucial, requiring the study of extreme events such as floods and droughts, as well as the rigor of winter and summer temperatures (Sánchez-Santillán., 2006). Due to the increase in the frequency of extreme hydrometeorological events associated with climate variability and climate change, as well as increased vulnerability in society, there is a need for greater interest in reducing greenhouse gas emissions. This article highlights the importance of raising awareness about disaster risks associated with climate, particularly precipitation. A visible change has occurred along the Usumacinta River, which is significant and for which there is no research on climate change, this study is important since it is the largest river in Mexico in terms of flow, and whose ecological condition still provides essential environmental services such as fishing (Mendoza-Carranza et al., 2018). Adaptation and risk management must be integral to the communities along this river to build resilient areas with a holistic vision that helps reduce vulnerability and mitigate the causes of climate change. The Usumacinta River serves as a natural border, dividing Mexico and Guatemala. It also forms a visible natural barrier separating the Yucatán Peninsula and serves as a commercial route. Thanks to its tributary rivers, there are significant commercial activities along its banks, as well as ecological connectivity through the tributary rivers that make up the largest watershed in Mexico by water volume. Additionally, its environmental water reserve ensures temporal connectivity across a vast area of wetlands and free-flowing rivers, which is crucial for biodiversity and ecological balance. Therefore, it was important to analyze the water availability in the area, which resulted in a very high level compared to the Falkenmark index. Thus, a flood study of the Usumacinta River was carried out using the modified Svanidze method, projecting a possible flood 28% greater than the largest flood recorded in this river, expected around the year 2040. This information is relevant for the inhabitants of the area, as it could help understand flood resilience at the community level as a tool to enhance the capacity of inhabitants along the Usumacinta River to cope with and anticipate possible flood events with minimal losses. Hence, this research could be replicated nationally and internationally with future extreme events to prevent populations, leading them toward climate resilience with minimal losses possible (Deepak et al., 2020; SEMARNAT, n.d., 2024). 2 STUDY AREA AND DATASET 2.1 Study area The Usumacinta River originates in Guatemala and flows into the Gulf of Mexico, spanning a total length of 1,123 km. It is the most water-abundant river in Mexico and Central America (Petrich., 2018). For 310 kilometers, the river forms a natural border between Guatemala and Mexico before entering the state of Tabasco. It remains navigable year-round from the Boca del Cerro canyon to its confluence with the Grijalva River, eventually emptying into the Gulf of Mexico at the Frontera Bar. The Usumacinta has an annual discharge of 55.832 billion m³, while the Grijalva River contributes an average flow of 27.013 billion m³/year (857 m³/s). Together, they form a significant delta covering 3,500 km² in the Pantanos of Centla biosphere reserve(Soares et al., 2017 ). For the purposes of this study, the section of the Usumacinta River from the Boca del Cerro canyon to its mouth at the Gulf of Mexico was considered, as this region holds significant implications for the populations residing along its banks (see Fig. 1 ). 2.2 Dataset The data for this study was sourced from historical records of weather stations and a gauging station provided by CONAGUA (National Water Commission) and SMN (National Meteorological Service). Additionally, baseline climate records and climate change models were obtained from the WorldClim® and UNIATMOS® websites. The DEM files were sourced from the U.S. government’s EarthExplorer website (earthexplorer.usgs.gov). 3 METHODOLOGY 3.1 Study area with hydrological availability. To determine the study area, an analysis of the average annual precipitation across the 37 hydrological-administrative regions of Mexico was conducted, utilizing data from 5,187 registered climatological stations. The annual average for each region was calculated, and the Falkenmark water stress indicator was applied to assess water availability. According to this indicator, a country with renewable water availability exceeding 10,000 m³ per person per year is classified as having high water availability. If the availability falls between 10,000 m³ and 5,000 m³ per person per year, it is considered moderate. Regions with less than 1,700 m³ per person per year are deemed to be under water stress, while less than 1,000 m³ indicates water scarcity, and less than 500 m³ signals absolute water scarcity (Falkenmark et al., 1989 ). Population data for each hydrological region was obtained from the National Institute of Statistics and Geography (INEGI., 2023) to determine per capita water availability. According to journalistic sources, the largest floods in Mexico typically occur between July, August, and November, especially in this area due to the overflow of the Usumacinta River. As a result, hydrological region 30 (RH-30) was selected for this study due to the frequency of floods and the significant human, economic, and social losses sustained year after year. With climate change likely to exacerbate these floods, a study on the region's climate resilience is crucial. Figure 2 highlights Mexico's hydrological regions, with RH-30 emphasized due to its high water availability and relevance to this study. 3.2 Trend projections for the weather stations in the study area Given the growth of towns and cities along the more than 1,000 kilometers of the Usumacinta River (Petrich, 2018), it is important to study potential future damage and impacts in the area. This is especially important because the region faces high levels of poverty and marginalization, making it more vulnerable to climate change, which has become more noticeable over the past two decades. Changes in rainfall patterns force us to develop new strategies for adapting to the climate. For this reason, it's important to study the weather stations closest to the Usumacinta River, which is the focus of this research. Five weather stations were analyzed using a trend projection method. This method helps to find the best equation that shows the relationship between dependent and independent variables. In this case, the equation is shown in Eq. 1 , where (a) represents the intercept, (b) is the partial regression coefficient, (x) is the independent variable (in this case, rainfall), and (y) is the dependent variable, which is the time period for each projection. Figure 3 shows the climate graph of the five weather stations that were studied, with data ranging from 1948 to 2014, depending on the station. $$\:\varvec{y}=\varvec{a}+\varvec{b}\varvec{x}$$ 1 3.3 Study of climate change The study of climate change requires understanding its impact, vulnerability, and adaptation. For this reason, climate change scenarios were projected for the study area using publicly available data. These projections aimed to link current climate conditions with future changes in hydrological region 30. The climate models used for the projections were CNRMCM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR, focusing on two emission scenarios (RCP 4.5 and RCP 8.5) with forecasts for the years 2039, 2069, and 2099. To generate the projections, climatology data and anomalies were downloaded from the websites https://www.worldclim.org and https://atlasclimatico.unam.mx . Using the QGIS software (which is freely available), the data were adjusted to fit hydrological region 30, and specific scales were applied to track changes in climate variables. The final projections combined both the base climate data and the anomalies. These were calculated using the raster calculator function in QGIS, and the results were saved in an Excel file to create the tables and graphs shown in Fig. 6 in the results section. 3.4 Homogeneity and trend analysis for gauging station “30019” The statistical properties of hydrological data series, including the mean, standard deviation, and serial correlation coefficients, can be influenced by trends in the mean or variance, as well as by sudden positive or negative shifts. Such anomalies often result from a loss of homogeneity and inconsistencies within the series. Typically, these issues are caused by human activities like deforestation, agricultural expansion, river channel alterations, dam construction, and increased greenhouse gas emissions. Natural events, such as wildfires, earthquakes, landslides, and volcanic eruptions, can also trigger these anomalies. To assess the homogeneity of the data series, statistical tests are employed to propose a null hypothesis, providing a method for its acceptance or rejection. In this study, eight homogeneity tests were used to evaluate potential changes in the recorded data, aiming to identify the occurrence of climate change and pinpoint when such changes began. Specifically, the Pettitt, Standard Normal, and Buishand tests were utilized to identify the year in which these shifts started. Additionally, two trend detection tests were applied, while the Anderson test was used to assess the independence of the series (see Table 1 c). These analyses were conducted at gauging station 30019 "Boca del Cerro" and five climate stations, with their identification numbers, names, and coordinates detailed in Tables 1 (a) and 1(b). Table 1 (a) Climatological Stations; (b) Gauging Station; (c) Tests applied to evaluate trend series at gauging station. a) Climatological study station data for the Usumacinta River Station ID Name UTM coordinates Altitude Hydrological region X (East) Y (North) 4024 Palizada, Palizada 595157.435 2012622.706 85 Grijalva-Usumacinta 7121 Nueva Esperanza, Ocosingo 752689.098 1820276.686 436 Grijalva-Usumacinta 27004 Boca del Cerro, (DGE) 657524.785 1928184.483 100 Grijalva-Usumacinta 27046 Tenosique, Tenosique 666332.128 1933788.253 60 Grijalva-Usumacinta 27050 Tres Brazos-Tabasco 541196.354 2032733.307 60 Grijalva-Usumacinta b) Station ID Name UTM Coordinates Altitude River Hydrological Region X (East) Y (North) 30019 Boca del Cerro 681787.4789 1909122.954 100 Usumacinta River Grijalva-Usumacinta C) Test Homogeneity tests Petit Standard normal test Statistician Buishand Von Newman test Fisher´s statistic Helmert t-student Cramer Trend tests Mann Kendall Spearman Independence Test Anderson Test 3.5 Flood Study and Synthetic Flow Rates. The study of floods is conducted by analyzing areas identified as prone to flooding. In these places, flood models are applied using statistical studies of records from hydrometric or meteorological stations, which allows for the preparation of a descriptive report on the geological, climatological situation, vegetation, and human settlements in the area in question. These studies also cover natural spaces and provide a historical perspective. Currently, these studies are usually carried out by the private industry, as many of their projects are at risk of flooding, which can result in significant economic losses, especially in equipment such as compressors or filters. To a lesser extent, flood studies are conducted for communities to determine potential human losses. In the case of populations located along the Usumacinta River, these losses occur recurrently. Therefore, it is crucial to carry out studies of synthetic or projected flows for the future to mitigate these economic losses. For this study; recorded and synthetic flows were analyzed using the modified Svanidze method (Domínguez-Mora., 2005). This method involves a two-step random procedure: first, the best-fit distribution function is used to generate \(\:\stackrel{-}{m}\) random values based on the distribution function of the total sum \(\:\stackrel{-}{n}\) . Second, a random selection is made from \(\:\stackrel{-}{m}\) historical years to derive the synthetic percentages of annual totals for each series and their corresponding monthly fractions. Once the synthetic annual totals are obtained, each total is multiplied by the corresponding annual percentage and the monthly fractions for the randomly selected year, resulting in \(\:\stackrel{-}{m}\) synthetic periodic series (Domínguez-Mora & Arganis-Juárez., 2009) This procedure is illustrated in Fig. 4 c. The study of the flood utilized data from gauging station 30019 “Boca del Cerro,” which has a 73-year record and is located 200 meters upstream of the southeast railway bridge in the town of Boca del Cerro, Tenosique Municipality, Tabasco. The hydrogram in Fig. 4 b was generated using the recorded maximum flow series shown in Fig. 4 a. River trace, direction, flow, and slope data were obtained using QGIS®, Google Hybrid®, and digital elevation models from www.earthexplorer.usgs.gov . This information was then imported into HECRAS, where the flood study was conducted using its RasMapper tool to generate 1D and 2D flood maps. 4 RESULTS 4.1 Availability of climatological and hydrometric data The quality, quantity, and availability of climate information have been important for the development of research (Leal-Nares et al., 2010 ), climatological stations provide invaluable value in decision-making in the presence of natural phenomena such as hydrometeorological events, they allow to anticipate and record the behavior of meteorological phenomena such as floods. In this study, over 30 years of records were collected from five climatological stations—4024, 7121, 27004, 27046, and 27050—as well as from the gauging station 30019, according to the World Meteorological Organization (WMO, 2021). Table 2 presents the recorded series and their corresponding statistics. The average annual accumulated rainfall across the five climatological stations is 1866.68 mm. For station 4024, the accumulated rainfall (Hp) deviates from the mean by approximately 394.19 mm, as shown in Table 2 b. The greatest average deviation in rainfall is observed at station 7121, with 498.83 mm (see Table 2 c). Gauging station 30019 records an average annual maximum flow (Q) of 5549.18 m³/s, with a standard deviation of 2897.57. The recorded annual maximum flow for this station is 9153 m³/s, while the minimum is 3566 m³/s, as indicated in Table 2 a. 4.2 Average annual precipitation in Hydrological Region 30. The study on the average annual precipitation in Hydrological Region 30 "Grijalva-Usumacinta" found that the region receives approximately 1,747,247 m³ of rainfall per year per km². According to data from the National Institute of Statistics and Geography (INEGI 2020), the region has a population of 6,557,760 and covers an area of 102,465 km². Based on this information, water availability was calculated to be 27,300.73 cubic meters per inhabitant per year, indicating a high level of water availability. 4.3 Results of climate change study in Hydrological Region-30 The results of the climate change study, conducted using WorldClim and UNIATMOS, indicated an increase in precipitation compared to the baseline climate, as illustrated in Fig. 5 . The data in Fig. 5 a show an average rainfall of 310 mm, a maximum average temperature of 33°C, and a minimum average temperature of 4°C, which represent the baseline climate for the area. Climate change projections were made using the following models: CNRM-CM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR. The GFDL-CM3 model for RCP 4.5, with a time horizon from 2045 to 2069, projected a 20 mm increase in precipitation compared to the baseline climate, while for RCP 8.5, the increase was 40 mm. These results are depicted in Figs. 5 b and 5 c for their respective concentration pathways. Regarding maximum temperatures, the baseline climate shows an average maximum temperature of 32°C. The most unfavorable data were provided by the HADGEM2-ES model for the periods 2045–2069 and 2075–2099 under RCP 4.5, indicating temperatures as high as 37°C, as shown in Fig. 5 d, representing a 5°C increase in maximum temperature. This is a critical issue to consider, especially due to the potential temperature rise during April, May, and June, which are key months for rainfed agriculture. In this region, 52% of the population lives in rural areas (Government of Mexico., 2009) and depends on seasonal rainfall. If temperatures increase, higher evapotranspiration will occur, leading to reduced aquifer recharge and limited water availability for irrigation, ultimately harming the local economy, which relies on livestock farming, forage crops, fishing, and seasonal agriculture. It is particularly important to address the rise in maximum temperature due to the risks it poses. Data for this variable can be seen in Figs. 5 d and 5 e for the corresponding RCPs. The baseline minimum temperature is 4°C on average for November, December, and January. Climate change models predict an increase in minimum temperatures of approximately 10°C, leading to a projected average minimum temperature of 14°C for these months, as shown in Figs. 5 f and 5 g. Overall, the climate change study indicates a slight increase in rainfall but a sharp rise in both maximum and minimum temperatures. This highlights the need to focus on climate resilience, enabling humans to adapt to climate variability. It also underscores the importance of implementing global public policies aimed at gradual decarbonization to mitigate the impacts humans have had on the planet through excessive consumption. 4.4 Hydrological Region-30. The trend analysis results reveal a significant increase in rainfall records from the five climatological stations analyzed. The data from stations 7121, 27004, and 27046, as well as gauging station 30019, showed an upward trend, as illustrated in Fig. 6a. In contrast, the series from climatological station 4024 exhibited a decreasing trend, as shown in Fig. 6b, while the records from station 27050 remained stable, as seen in Fig. 6c. Based on these findings, there is evidence of an increasing trend in precipitation in the area, highlighting noticeable changes in climate variability. This is supported by the results of the water availability study, which revealed a significantly high-water supply, exceeding the threshold of 10,000 cubic meters per person per year, as per the Falkenmark index. This high availability aligns with the results of the climate change study, which indicates a significant rise in rainfall. Consequently, this study underscores the importance of understanding future uncertainties, particularly the challenges humanity faces due to changes and fluctuations in precipitation patterns. This should raise awareness of the critical importance of water as a resource for human survival, and how its excess can lead to natural disasters such as floods, which result in the loss of human lives. 4.5 Homogeneity and trend test results for gauging station 30019 To perform the homogeneity and trend analysis, a time series with a minimum of 30 consecutive years of records is required (WMO., 2017), which is met in this case, as 66 years of data have been recorded, as shown by the maximum annual flows in Table 3 a. Based on this, homogeneity and trend tests were applied to gauging station 30019. The results indicated that it is a non-stationary series, as shown in Table 3 b. The Pettitt test revealed a break in the year 1971, while the Standard Normal and Buishand Statistical tests identified changes starting from 1964. For the trend tests, Mann-Kendall and Spearman were applied, with the latter showing a decreasing trend. However, it is important to note that this test was not deterministic, as the trend analysis indicates an overall increasing trend. Therefore, based on the studies conducted, it is inferred that there is a shift with an upward trend. The independence test confirmed that the series is completely independent. 4.6 Flood study results with measured and predicted flow rates. During the flood study, using the recorded annual maximum flow, the Froude number averaged less than 1, indicating the river was in a subcritical regime with low velocities, high water levels, and minimal sediment transport, as shown in Fig. 7 a. The flood hydrograph for this study, recorded on October 24, 2008, at gauge station 30019, coincided with the peak flow of the series, leading to significant flooding along the Usumacinta River. This was corroborated by news reports on October 21 of that year (Rivero Zapata., 2008), as depicted in Fig. 7 h. During this time, the Usumacinta River flooded Tenosique, Tabasco, a locality with a population of 60,000 as recorded in 2008 (INEGI., 2023). Using the modified Svanidze method for flood forecasting, a maximum flow was identified among the top 100 random numbers generated in the synthetic series, exceeding the previously recorded flow. This resulted in an increase in the critical water level and a slight rise in the Froude number, as shown in Fig. 7 b. Based on these results, the maximum flow obtained from the synthetic series was 11,735.51 m³/s, ranking 18th among the synthetic flows (Fig. 7 i). By adding 18 years to the last year of recorded data, 2022, it is inferred that a similar flood event could occur in 2040. It is important to note that official flow records are only available up to 2014; however, a review of gauge station 30019’s facilities revealed data up to 2022. When comparing the results from 2015 to 2022 with the synthetic data generated by the modified Svanidze method, a 95% accuracy was observed. The flood study for the synthetic flow revealed slightly higher velocities and an increased critical water level for most sections of the river, with the Froude number slightly exceeding 1. This suggests that a flood more severe than the one in 2008 could occur. Figures 7 c and 7 d illustrate the critical water level along the Usumacinta River, with Fig. 7 c showing the actual maximum recorded flow, and Fig. 7 d displaying the synthetic maximum flow. The actual velocity results are shown in Fig. 7 e, while the synthetic velocity results are in Fig. 7 f. Notably, velocities obtained with the actual maximum recorded flow exceeded 12 m/s, as indicated by the red zone in the velocity map in Fig. 7 e. Velocity data is crucial for assessing potential damage in flood-prone areas using the Dorrigo diagram, which defines five velocity ranges, as shown in Fig. 7 g. In this diagram, point A marks where vehicle instability begins, making it moderately safe for pedestrians to walk, with a maximum safe velocity of 2 m/s. Point B indicates walking is unsafe, and beyond point C, structural damage to lightweight constructions occurs (Cruz García, 2019). If the velocities in the red-marked zones of Figs. 7 e and 7 f exceed 12 m/s, as compared to the Dorrigo diagram, structural damage, vehicle loss, and, most critically, loss of human lives will occur, as water at this speed will penetrate localities along the Usumacinta River. This scenario unfolded in Tenosique, Tabasco, in October 2008. Therefore, it is crucial to remain vigilant due to climatic variability to mitigate losses of human life and property. Urban sprawl near the Usumacinta River has increased, as shown in Figs. 7 e and 7 f, further raising vulnerability to climate change. According to the results of this study, water availability in the area is 27,300.73 cubic meters per inhabitant per year, indicating very high-water availability, as it exceeds the 10,000 cubic meters per inhabitant per year threshold set by the Falkenmark index for regions with abundant water resources. Consequently, the likelihood of future floods is high, highlighting the need for increased awareness of these meteorological phenomena. In the climate change study using the CNRMCM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR models under RCP 4.5 and RCP 8.5 scenarios, projections for 2039, 2069, and 2099 forecast a 20 mm increase in precipitation for August and September compared to baseline climate data. Regarding maximum temperatures, the models predicted a 5°C increase, with future maximum temperatures projected to reach 37°C, compared to the baseline of 32°C. This suggests greater evapotranspiration, which may alter precipitation patterns and contribute to biodiversity loss in the region, threatening plant, animal, and insect species that are crucial for maintaining ecological balance. Precipitation projections from climatological stations show an upward trend, indicating an increased risk of flooding in the area. Similarly, projections from the gauge station also reflect rising trends, leading to the conclusion that the region is flood-prone. Studies on water availability, climate change, and the increasing trends observed in climatological stations, as well as the homogeneity and trend analysis from the gauge station, indicate that significant changes have occurred since 1964. These findings suggest that climate variability has been impacting the area since that time, as evidenced by the shifts in the homogeneity study of the flow series. This is further supported by the floods that have occurred over the past 20 years, with the largest flood event taking place in October 2008. According to forecasts generated by the modified Svanidze model, such events may recur with greater intensity around 2040. Synthetic data suggest a flow 28% higher than the maximum recorded flow, which implies that future floods could cause 28% more damage than the October 2008 flood. Given this, it is crucial to prepare society for these extreme weather events and take proactive measures to mitigate their impacts. 5 DISCUSSION 5.1 Comparison with previous studies The results of this study indicate a significant increase in precipitation and, consequently, in the flow rates of the Usumacinta River, which could lead to a higher risk of flooding. These findings are consistent with previous research that has addressed similar issues in other regions. For example, Abram et al., ( 2016 ) found that human-induced global warming has led to an increase in the frequency of extreme events, including intense rainfall and prolonged droughts. Their studies, focused on various regions worldwide, demonstrate a climate change pattern. Like in this study, Abram et al., 2016 . observed an increase in precipitation during the wettest months, suggesting a global trend toward a more extreme climate. Deepak et al., ( 2020 ) conducted a geospatial analysis of flood vulnerability in several river basins and found that areas with high climate variability present a higher risk of extreme hydrometeorological events. Similarly, our study projects a 28% increase in the maximum flow of the Usumacinta River by the year 2040. Another relevant study is that of Sánchez-Cohen & Díaz-Padilla, ( 2008 ), who analyzed climate variability in Mexico and its hydrological impacts. They found that changes in precipitation patterns have led to a higher frequency of floods and droughts in various regions of the country. This results, showing an increase in precipitation and maximum temperatures in the Usumacinta River region, corroborate Sánchez-Cohen & Díaz-Padilla, ( 2008 ) conclusions, highlighting the need for adaptation strategies to address climate change. Additionally, Petrich, (2018) research on the Usumacinta River emphasized the importance of integrated water resource management and the construction of resilient infrastructure to mitigate flood impacts. Our findings, projecting a significant increase in flood risk for the coming decades, reinforce the urgency of implementing Petrich, (2018) recommendations to strengthen the resilience of local communities to extreme climatic events. In summary, the results of this study are consistent with the existing literature and underscore the importance of continuing research and applying mitigation and adaptation measures to address the challenges of climate change. The increasing trends of precipitation and maximum temperatures in the Usumacinta River region is a local manifestation of a global phenomenon that requires coordinated and sustained action at both regional and global levels. 5.2 Practical implications The findings of this study have several important practical implications that must be considered to improve the resilience of communities and infrastructure in the Usumacinta River region. Flood-resilient infrastructure: the projection of a 28% increase in the maximum flow of the Usumacinta River by the year 2040 underscores the urgent need to strengthen and adapt existing infrastructure to withstand flood events. This includes the construction and enhancement of levees, dams, and drainage systems capable of handling higher flow rates and preventing catastrophic overflows. Additionally, it is crucial to incorporate green infrastructure designs, such as buffer zones and wetlands, that can absorb and slow down water flow during extreme events. Urban planning and land use: local and regional authorities must review and update land use plans to limit development in flood-prone areas. Implementing zoning policies that discourage construction in high-risk zones and promote development in less vulnerable areas is essential. Furthermore, urban planning should include clear and accessible evacuation routes and the creation of open spaces that can serve as water retention areas during flood events. Early warning systems and community preparedness: establishing robust and efficient early warning systems is fundamental to reducing the vulnerability of local communities. These systems should include real-time meteorological and hydrological monitoring technologies that can detect increases in river flow and issue alerts well in advance, allowing communities to take preventive measures. Community education and training programs on flood preparedness and response are equally crucial for enhancing population resilience. Integrated water resource management: water resource management in the Usumacinta River region must be integrated and coordinated among various governmental entities and local organizations. Implementing management strategies that consider both water availability and the need to reduce flood risk is necessary. This can include regulating water extraction, protecting watersheds, and restoring natural ecosystems that can act as flood buffers. Environmental policies and regulations Given that climate change is a key factor contributing to climate variability and increased flood risks, it is crucial that national and regional environmental policies and regulations focus on mitigating the effects of climate change. This includes reducing greenhouse gas emissions, promoting renewable energy, and implementing sustainable practices in agriculture and industry. Continuous research and monitoring: ongoing research and monitoring are essential for improving our understanding of climate patterns and their impact on the Usumacinta River. Investing in long-term studies and advanced monitoring technologies is necessary to collect accurate data that can inform management strategies and public policies. Additionally, collaboration with academic and research institutions can provide valuable insights and develop innovative solutions to address climate change challenges. The results of this study highlight the need for immediate and coordinated actions to address flood risks in the Usumacinta River region. Implementing these practical implications will not only contribute to the protection of communities and infrastructure but also promote sustainable and resilient development in the face of climate change impacts. 5.3 Study limitations Despite the significant findings of this study, several limitations must be recognized to provide a balanced view of the results and their implications. Dependence on historical data: the accuracy of our climate and flow projections largely depends on the historical data collected from meteorological and gauging stations. The quality and availability of these data can vary which may introduce uncertainties into our predictive models. Additionally, some historical data may be subject to measurement errors or lack of continuous records, which can affect the consistency of our conclusions. Climate models and assumptions The use of climate models such as CNRMCM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR involves certain assumptions about future greenhouse gas emission scenarios (RCP 4.5 and RCP 8.5). These assumptions may not fully capture the complexity of climate interactions and local variabilities specific to the Usumacinta River. Therefore, precipitation and temperature projections should be interpreted with caution and considered as possible scenarios rather than definitive predictions. Spatial and temporal variability the spatial and temporal variability of extreme climatic events cannot always be accurately captured in a regional study. Differences in topography, land use, and local hydrological characteristics can influence the magnitude and frequency of floods, which may not be fully reflected in our models. This limitation can lead to an underestimation or overestimation of flood risks in certain areas. Changes in land use and human activities changes in land use, such as urbanization, deforestation, and agricultural practices, can significantly impact river dynamics and flood patterns. Our study does not exhaustively address how these anthropogenic changes may interact with climatic factors to influence flood risks. Future research should consider these factors to obtain a more comprehensive understanding of the risks. Scalability of results while this study focuses on the Usumacinta River region, the scalability of the results to other river basins may be limited due to differences in climatic, geographic, and socioeconomic conditions. The proposed adaptation and mitigation strategies must be tailored to the specific characteristics of each region to be effective. Lack of consideration of socioeconomic impacts although this study focuses on climate and flow projections, it does not thoroughly address the socioeconomic impact of floods on local communities. Evaluating social and economic vulnerability, as well as post-disaster recovery strategies, are crucial aspects that should be considered in future research to develop a comprehensive response to flood risks. Although this study provides valuable information on future climate and flow patterns in the Usumacinta River region, it is essential to consider these limitations when interpreting the results. Acknowledging these limitations not only improves the study's transparency but also guides future research toward areas needing more detailed and robust analysis. 5.4 Future research directions The findings of this study open various opportunities for future research that can complement and expand our understanding of flood risks in the Usumacinta River region. Some key areas for future research include: Integration of land use changes Given that human activities, such as urbanization and deforestation, significantly impact the basin's hydrology, it is crucial to investigate how these changes affect precipitation and flow patterns. Future studies should develop models that integrate land use data and its evolution, allowing for more accurate flood predictions and more effective land use planning. Assessment of socioeconomic vulnerability: it is essential to conduct studies that assess the socioeconomic vulnerability of local communities to floods. Research analyzing factors such as community response capacity, critical infrastructure, and social resilience mechanisms can provide a more comprehensive view of flood impacts and help design more effective adaptation strategies. Continuous and enhanced hydrological data monitoring: implementing advanced and continuous hydrological monitoring systems in the Usumacinta River basin is a priority. Future research should focus on developing and deploying sensor technologies and monitoring networks that can provide real-time data on flows, water levels, and precipitation. These data will improve predictive model accuracy and early responses to extreme events. Impact of climate change on biodiversity: Given the crucial role of the Usumacinta River in regional biodiversity, it is important to investigate how climate change and flood events affect aquatic and terrestrial ecosystems. Studies evaluating the impact on key species, ecological connectivity, and ecosystem services can help develop conservation and restoration strategies that mitigate the negative effects of climate change. Development of multivariable predictive models: future research should explore the development of predictive models that incorporate multiple variables, such as precipitation, temperature, land use, and topographical features. These models can improve the accuracy of flood predictions and allow for the simulation of different climate change scenarios and their impact on the Usumacinta River basin. Interregional comparative studies: interregional comparative studies should be conducted to compare findings from the Usumacinta River region with other river basins at national and international levels, as this can provide valuable insights into similarities and differences in flood patterns and their causes. These comparative studies can help identify best practices and management strategies that can be adapted to different contexts. Effectiveness of adaptation strategies: evaluating the effectiveness of current and future adaptation strategies is crucial to ensuring the resilience of local communities. Research analyzing the implementation and impact of measures such as resilient infrastructure construction, reforestation, and land use policies can offer evidence-based recommendations to improve flood risk planning and management. Community participation and perception: the participation of local communities in flood risk planning and management is fundamental. Future research should explore community perceptions of climate change and floods, as well as their willingness to participate in adaptation initiatives. This can help design more effective education and awareness programs that promote collaboration and community empowerment. These future research directions are essential to advancing our understanding and management capabilities of flood risks in the Usumacinta River region. Interdisciplinary collaboration and a focus on practical, evidence-based solutions are crucial for addressing climate change challenges and protecting both human communities and natural ecosystems. 6 CONCLUSIONS This study has provided an in-depth analysis of climate variability in the Usumacinta River basin, underscoring the significant impact of climate change on precipitation and more flow patterns. The key findings and their implications are summarized as follows: • Increase in precipitation and more flows : A rising trend in precipitation has been observed at the five climatological stations analyzed, along with an increase in Usumacinta River flows since 1964. Climate change models project up to a 40 mm rise in precipitation and a 5°C increase in maximum temperatures by the mid-21st century, significantly heightening the region's flood risk. • Flood risk : Projections using the modified Svanidze method suggest that maximum annual flows could increase by 28% by 2040. This potential rise emphasizes the urgent need to implement adaptation measures to protect local communities and critical infrastructure from future flood events. • Implications for water resource management : While high water availability in the region benefits certain economic activities, it also poses challenges for flood risk management. Developing an integrated approach to water resource management is crucial, balancing flood mitigation strategies with long-term sustainability goals. • Need for resilient infrastructure : To reduce the impact of future floods, investment in resilient infrastructure is essential. This includes improving levees, drainage systems, and creating natural buffer zones. Urban planning should also prioritize zoning policies that restrict development in flood-prone areas. • Enhancing community resilience : Strengthening early warning systems, community education programs, and evacuation plans is vital for improving local resilience to floods. Engaging communities in disaster preparedness and response planning can significantly enhance their capacity for recovery after flood events. • Public policies and climate change : Developing and implementing public policies that address climate change is critical to reducing the region's vulnerability. This includes promoting sustainable practices, cutting greenhouse gas emissions, and protecting natural ecosystems that serve as natural flood barriers. In conclusion, this study highlights the need for a coordinated, multidisciplinary approach to addressing the challenges posed by climate change in the Usumacinta River basin. Immediate and strategic actions are required to enhance resilience to future floods, thereby safeguarding both communities and the environment. The findings also highlight several areas that warrant further research, such as the integration of land-use changes, socioeconomic vulnerability assessments, continuous hydrological and climate monitoring, and the development of multivariate predictive models. These efforts will provide a more comprehensive understanding of flood risks and enable more effective management in the region. Declarations ACKNOWLEDGEMENTS Acknowledgments to the National Autonomous University of Mexico, CONAHCYT, and the Institute of Engineering for the support provided to carry out this research. DATA AVAILABILITY STATEMENT Data can be made publicly available. CONFLICT OF INTEREST The authors declare there is no conflict. DECLARATION OF GENERATIVE AI IN SCIENTIFIC WRITING Statement: During the preparation of this work the author(s) used Academic GPT in order to review the writing process. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication References Abram NJ, McGregor HV, Tierney JE, Evans MN, McKay NP, Kaufman DS, Thirumalai K, Martrat B, Goosse H, Phipps SJ, Steig EJ, Kilbourne KH, Saenger CP, Zinke J, Leduc G, Addison JA, Mortyn PG, Seidenkrantz MS, Sicre MA, Von Gunten L (2016) Early onset of industrial-era warming across the oceans and continents. Nature 536(7617):411–418. https://doi.org/10.1038/nature19082 Caso-Chávez M, González-Terrazas DI (2022) Guía sobre escenarios de cambio climático. INECC Cassagne JC (2003) El daño ambiental colectivo. Ius et Veritas Deepak S, Rajan G, Jairaj PG (2020) Geospatial approach for assessment of vulnerability to flood in local self-governments. 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Aposta Revista de Ciencias Sociales 59:1–35. http://www.redalyc.org/articulo.oa?id=495950255002 Government of Mexico (2023) Población Tenosique Tabasco . https://tabasco.gob.mx/tenosique INECC (2019) Atlas Nacional de Vulnerabilidad ante el Cambio Climático en México (1ª ed., Vol. 1). https://atlasvulnerabilidad.inecc.gob.mx/page/fichas/ANVCC_LibroDigital.pdf INEGI (2023) INEGI DATA: Población . https://www.inegi.org.mx/app/geo2/ahl/ Leal-Nares OA, Mendoza ME, Carranza González E (2010) Análisis y modelamiento espacial de información climática en la cuenca de Cuitzeo, México. Revista del Instituto de Geografía, vol 72. UNAM, pp 49–67 Mendoza-Carranza M, Arévalo-Frías W, Espinoza-Tenorio A, Hernández-Lazo CC, Álvarez-Merino AM, Rodiles-Hernández R (2018) The importance and diversity of fisheries resources at the Usumacinta River, Mexico. Revista Mexicana de Biodiversidad 89:S131–S146. https://doi.org/10.22201/ib.20078706e.2018.0.2245 Millano J, Paredes F, Vivas I (2007) Efecto de la Oscilación del Sur (ENSO) y la temperatura superficial del Atlántico en la distribución espacio-temporal de las lluvias en el estado Cojedes. Revista AGROLLANIA 4:103–106 ONU (2023) Población. Una población en crecimiento . https://www.un.org/es/global-issues/population Petrich P (2018a) El río Usumacinta: confluencia de historias. Cuad LIRICO 18. https://doi.org/10.4000/lirico.5577 Petrich P (2018b) El río Usumacinta: confluencia de historias. Cuad LIRICO 18. https://doi.org/10.4000/lirico.5577 Rivero Zapata J (2008) 21 de octubre). Tenosique inundado . Jorgerivero.wordpress.com. https://jorgerivero.wordpress.com/2008/10/21/tenosique-inundadoechele-un-vistazo-asi-esta-ahora/ Sánchez-Cohen I, Díaz-Padilla G (2008) Variabilidad climática en México: algunos impactos hidrológicos, sociales y económicos. Ingeniería Hidráulica en México. XXIII, pp 5–24 Sánchez-Santillán N, Santamaría-Pérez MG, Galindo-López R (2006) La Oscilación del Atlántico Norte: un fenómeno que incide en la variabilidad climática de México. Ingeniería Investigación y Tecnología VII(2):85–95 SEMARNAT (2024) Balance de agua . https://apps1.semarnat.gob.mx:8443/dgeia/informe_resumen/07_agua/cap7.html SEMARNAT (2024) Disponibilidad del agua . https://paot.org.mx/centro/ine-semarnat/informe02/estadisticas_2000/informe_2000/04_Agua/4.4_Disponibilidad/index.htm Soares D, García A, Coordinadores G (eds) (2017) La cuenca del río Usumacinta desde la perspectiva del cambio climático (1ª ed.). Instituto Mexicano de Tecnología del Agua. http://www.imta.gob.mx WMO (2017) WMO guidelines on the calculation of climate normals . World Meteorological Organization WMO. (2021) 5 de mayo). The updated 30-year reference period reflects the changing climate . https://public.wmo.int/en/media/news/updated-30-year-reference-period-reflects-changing-climate Tables Tables 2 and 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files GA.png GRAPHICAL ABSTRACT Table23.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6579639","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":452906137,"identity":"e3ad84d5-e910-4a72-ae18-774d5254b84a","order_by":0,"name":"Yaridalia Ramirez Abundis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJCCAzwMFjwM7A1ApoEF0VokeBh4DoC0SBBpDVALA4NEAohJhBaD42cfHnhTIyHDP/P51Q0/CiQY+Nu7E/BrOZNucHDOMQkeids5ZTd7gA6TOHN2A34tB9IYDvOwAf1yOyftBg9Qi4FELgEt558BtfyT4JG/eSbt5h+itNwA2sLbJsFjcIP92G2ibJG88Yzh4Nw+CR7DMzlst2UMgJ4i5Be+82nMH958s7GXO3782c03f2zk+Nt78WtROABn8hiASbzKQUC+Ac5kf0BQ9SgYBaNgFIxMAACmrkiuFoYpggAAAABJRU5ErkJggg==","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":true,"prefix":"","firstName":"Yaridalia","middleName":"Ramirez","lastName":"Abundis","suffix":""},{"id":452906138,"identity":"2c7077d1-003f-467c-8a12-44ef89ae5143","order_by":1,"name":"Maritza Liliana Arganis Juárez","email":"","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":false,"prefix":"","firstName":"Maritza","middleName":"Liliana Arganis","lastName":"Juárez","suffix":""},{"id":452906139,"identity":"9603f6c6-3ebb-4cdf-a3c6-3a6d64c9f3ec","order_by":2,"name":"Ramon Domínguez Mora","email":"","orcid":"","institution":"National Autonomous University of Mexico","correspondingAuthor":false,"prefix":"","firstName":"Ramon","middleName":"Domínguez","lastName":"Mora","suffix":""}],"badges":[],"createdAt":"2025-05-02 15:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6579639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6579639/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82360422,"identity":"697f190a-b33f-4945-ab58-bfa1b166724f","added_by":"auto","created_at":"2025-05-09 11:38:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":887142,"visible":true,"origin":"","legend":"\u003cp\u003eLocation Map of the Hydrological Region 30 of Mexico\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/8f2a5a61865af8fb1725ff91.png"},{"id":82360386,"identity":"27d18f81-b004-42dd-832c-012f3039e9fe","added_by":"auto","created_at":"2025-05-09 11:38:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124600,"visible":true,"origin":"","legend":"\u003cp\u003eHydrological availability in each hydrological region of Mexico.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/5181310fc8fe53e509f94a3e.png"},{"id":82359614,"identity":"809711d3-9196-4625-8942-b57db73e2e25","added_by":"auto","created_at":"2025-05-09 11:30:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100145,"visible":true,"origin":"","legend":"\u003cp\u003eThe five weather stations in the study area; (a) Climogram for station 4024; (b) for weather station 7121; (c) for weather station 27004; (d) for weather station 27050; (e) for weather station 27046.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/a58451d38e1d2c74e6e15845.png"},{"id":82360392,"identity":"eefe19f2-dc53-4113-a756-4d20e55a4288","added_by":"auto","created_at":"2025-05-09 11:38:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":319655,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of flow rates and flood predictions for gauging station 30019;(a) Time series of annual maximum flow rates recorded at station 30019; (b) Flood hydrographs of gauging station 30019, showing peak flows and their recurrence intervals for what was the maximum flow that caused the flood; (c) application of the modified Svanidze method, steps, calculations, emphasize the methodology and results; (d) Graph with modified Svanidze method with flow rates and synthetic flow rates for future floods:\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/6f56b6962e9cc8de299d19f8.png"},{"id":82359652,"identity":"31521f97-7dcd-4db3-8c2d-d75d0f3c02f7","added_by":"auto","created_at":"2025-05-09 11:30:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":194257,"visible":true,"origin":"","legend":"\u003cp\u003eClimate change in the area: (a) Baseline climate from 1950 to 2000; (b)Projection of precipitation with RCP 4.5; (c) Projection of precipitation with RCP 8.5; (d) Projection for maximum temperature with RCP 4.5; (e) Projection for maximum temperature with RCP 8.5; (f) Projection for minimum temperature with RCP 4.5; (g) Projection for minimum temperature with RCP 8.5.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/9f0a9de1588c1a9b269d0c1d.png"},{"id":82360401,"identity":"67484525-0981-4c16-a48a-160c87050d10","added_by":"auto","created_at":"2025-05-09 11:38:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":129761,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of precipitation and flow; (a) projections with an increasing trend of both precipitation and flows: (b) projections with a decreasing trend; (c) without projection with zero slope.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/874fe2b537508058a9ebc4a3.png"},{"id":82360442,"identity":"45024f48-7c65-45b8-99c2-27750d352a5c","added_by":"auto","created_at":"2025-05-09 11:38:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2604714,"visible":true,"origin":"","legend":"\u003cp\u003eStudy results with Hecras® and Qgis®; (a) Flow results; (b) Forecast flow results; (c) Depth flow; (d) Depth forecast flow; (e) Flow velocities; (f) Forecast flow velocities; (g) Risk ratio associated with flood depth and velocity; (h) News October 2008 floods in Tenosique, Tabasco; important city, located on the banks of the Usumacinta River; (i) Forecast flow with the modified Svanidze method.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/9758064c4e96aacdd0be1c53.png"},{"id":84951048,"identity":"5f513ba1-3295-41e2-a9ee-2dcd1f370a34","added_by":"auto","created_at":"2025-06-19 07:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5095704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/90fbe82f-28d3-4424-9fe2-7acbfc05e14a.pdf"},{"id":82360389,"identity":"d4cb456d-2719-4171-a4dd-5820506f38b7","added_by":"auto","created_at":"2025-05-09 11:38:29","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":126419,"visible":true,"origin":"","legend":"\u003cp\u003eGRAPHICAL ABSTRACT\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/da0e98b6aff87a9f141450c5.png"},{"id":82360394,"identity":"f06b919e-f24e-43ca-a0a1-fbc297464a43","added_by":"auto","created_at":"2025-05-09 11:38:30","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44083,"visible":true,"origin":"","legend":"","description":"","filename":"Table23.docx","url":"https://assets-eu.researchsquare.com/files/rs-6579639/v1/32f8ae0555fbf0efedba8c2e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydroclimatic Risk Analysis in the Usumacinta River Basin","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eSigns of climate change\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePromote actions to reduce effects of climate change\u003c/li\u003e\n \u003cli\u003eThe change in rainfall patterns causes floods which alters the environment and makes cohabitation difficult.\u003c/li\u003e\n \u003cli\u003eForecast future floods with climate patterns.\u003c/li\u003e\n \u003cli\u003eIdentify the available rainfall for an integrated management of the water resource.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 INTRODUCTION","content":"\u003cp\u003eThe first studies on climate modeling for Mexico were conducted at the UNAM Atmospheric Sciences Center, initially using the Climate Thermodynamics Model. Subsequently, a country study was developed, diagnosing the vulnerability of coastal areas (Caso-Chávez et al., 2022). The most relevant international studies for Mexico are the National Communications reported to the United Nations Framework Convention on Climate Change (CMNUCC); these reports have facilitated multidisciplinary studies on potential impacts in Mexico, describing current climate and future projections based on scenarios from the IPCC Fourth Assessment Report (SRES) and Fifth Assessment Report (RCP). \u0026nbsp;One notable effort at the state level is the \"State Climate Action Programs,\" which conduct regional analyses of climate change scenarios to comply with Mexico's General Climate Change Law, establishing that states must have such programs (Article 8). The National Climate Change System (SINACC) aims to foster synergies to address the country's vulnerability and risks and prioritize mitigation and adaptation actions to climate change (Salib, 2020).\u003c/p\u003e\n\u003cp\u003eThe North Atlantic Oscillation (NAO) is a periodic atmospheric phenomenon characterized by movements of large atmospheric air masses on an intercontinental scale, with a decisive impact on climatology. Its nature is like that of the El Niño-Southern Oscillation (ENSO), although much less known. The first official scientific description of both events is attributed to the same person, the English meteorologist Sir Gilbert Thomas Walter, who published it in an article in 1923. Despite the limited number of studies on the North Atlantic Oscillation (NAO), its crucial effect on climate variability in the regions it encompasses should not be underestimated. These regions include North America (Mexico, United States, and Canada) on the northern side of the Atlantic Ocean and Northwestern Europe on the opposite side of the continent. One of the factors that has brought attention to the topic of the North Atlantic Oscillation (NAO) is its likely relationship with the current atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration. Another factor is the possibility that its influence on global climate extends beyond the North Atlantic, due to its potential connection with El Niño event. Given that the Mexican Republic falls within the North Atlantic Oscillation's influence zone, study results have provided critical information demonstrating climate variation in Mexico. Therefore, short- and long-term rainfall forecasting is crucial, requiring the study of extreme events such as floods and droughts, as well as the rigor of winter and summer temperatures (Sánchez-Santillán., 2006).\u003c/p\u003e\n\u003cp\u003eDue to the increase in the frequency of extreme hydrometeorological events associated with climate variability and climate change, as well as increased vulnerability in society, there is a need for greater interest in reducing greenhouse gas emissions. This article highlights the importance of raising awareness about disaster risks associated with climate, particularly precipitation. A visible change has occurred along the Usumacinta River, which is significant and for which there is no research on climate change, this study is important since it is the largest river in Mexico in terms of flow, and whose ecological condition still provides essential environmental services such as fishing (Mendoza-Carranza et al., 2018). Adaptation and risk management must be integral to the communities along this river to build resilient areas with a holistic vision that helps reduce vulnerability and mitigate the causes of climate change. The Usumacinta River serves as a natural border, dividing Mexico and Guatemala. It also forms a visible natural barrier separating the Yucatán Peninsula and serves as a commercial route. Thanks to its tributary rivers, there are significant commercial activities along its banks, as well as ecological connectivity through the tributary rivers that make up the largest watershed in Mexico by water volume. Additionally, its environmental water reserve ensures temporal connectivity across a vast area of wetlands and free-flowing rivers, which is crucial for biodiversity and ecological balance. Therefore, it was important to analyze the water availability in the area, which resulted in a very high level compared to the Falkenmark index. Thus, a flood study of the Usumacinta River was carried out using the modified Svanidze method, projecting a possible flood 28% greater than the largest flood recorded in this river, expected around the year 2040. This information is relevant for the inhabitants of the area, as it could help understand flood resilience at the community level as a tool to enhance the capacity of inhabitants along the Usumacinta River to cope with and anticipate possible flood events with minimal losses. Hence, this research could be replicated nationally and internationally with future extreme events to prevent populations, leading them toward climate resilience with minimal losses possible (Deepak et al., 2020; SEMARNAT, n.d., 2024).\u003c/p\u003e"},{"header":"2 STUDY AREA AND DATASET","content":"\n\u003ch3\u003e2.1 Study area\u003c/h3\u003e\n\u003cp\u003eThe Usumacinta River originates in Guatemala and flows into the Gulf of Mexico, spanning a total length of 1,123 km. It is the most water-abundant river in Mexico and Central America (Petrich., 2018). For 310 kilometers, the river forms a natural border between Guatemala and Mexico before entering the state of Tabasco. It remains navigable year-round from the Boca del Cerro canyon to its confluence with the Grijalva River, eventually emptying into the Gulf of Mexico at the Frontera Bar. The Usumacinta has an annual discharge of 55.832\u0026nbsp;billion m\u0026sup3;, while the Grijalva River contributes an average flow of 27.013\u0026nbsp;billion m\u0026sup3;/year (857 m\u0026sup3;/s). Together, they form a significant delta covering 3,500 km\u0026sup2; in the Pantanos of Centla biosphere reserve(Soares et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the purposes of this study, the section of the Usumacinta River from the Boca del Cerro canyon to its mouth at the Gulf of Mexico was considered, as this region holds significant implications for the populations residing along its banks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Dataset\u003c/h2\u003e \u003cp\u003eThe data for this study was sourced from historical records of weather stations and a gauging station provided by CONAGUA (National Water Commission) and SMN (National Meteorological Service). Additionally, baseline climate records and climate change models were obtained from the WorldClim\u0026reg; and UNIATMOS\u0026reg; websites. The DEM files were sourced from the U.S. government\u0026rsquo;s EarthExplorer website (earthexplorer.usgs.gov).\u003c/p\u003e"},{"header":"3 METHODOLOGY","content":"\u003cp\u003e3.1 \u003cstrong\u003eStudy area with hydrological availability.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the study area, an analysis of the average annual precipitation across the 37 hydrological-administrative regions of Mexico was conducted, utilizing data from 5,187 registered climatological stations. The annual average for each region was calculated, and the Falkenmark water stress indicator was applied to assess water availability. According to this indicator, a country with renewable water availability exceeding 10,000 m\u0026sup3; per person per year is classified as having high water availability. If the availability falls between 10,000 m\u0026sup3; and 5,000 m\u0026sup3; per person per year, it is considered moderate. Regions with less than 1,700 m\u0026sup3; per person per year are deemed to be under water stress, while less than 1,000 m\u0026sup3; indicates water scarcity, and less than 500 m\u0026sup3; signals absolute water scarcity (Falkenmark et al., \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e). Population data for each hydrological region was obtained from the National Institute of Statistics and Geography (INEGI., 2023) to determine per capita water availability.\u003c/p\u003e\n\u003cp\u003eAccording to journalistic sources, the largest floods in Mexico typically occur between July, August, and November, especially in this area due to the overflow of the Usumacinta River. As a result, hydrological region 30 (RH-30) was selected for this study due to the frequency of floods and the significant human, economic, and social losses sustained year after year. With climate change likely to exacerbate these floods, a study on the region's climate resilience is crucial. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e highlights Mexico's hydrological regions, with RH-30 emphasized due to its high water availability and relevance to this study.\u003c/p\u003e\n\u003ch3\u003e3.2 Trend projections for the weather stations in the study area\u003c/h3\u003e\n\u003cp\u003eGiven the growth of towns and cities along the more than 1,000 kilometers of the Usumacinta River (Petrich, 2018), it is important to study potential future damage and impacts in the area. This is especially important because the region faces high levels of poverty and marginalization, making it more vulnerable to climate change, which has become more noticeable over the past two decades. Changes in rainfall patterns force us to develop new strategies for adapting to the climate. For this reason, it's important to study the weather stations closest to the Usumacinta River, which is the focus of this research.\u003c/p\u003e\n\u003cp\u003eFive weather stations were analyzed using a trend projection method. This method helps to find the best equation that shows the relationship between dependent and independent variables. In this case, the equation is shown in Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, where (a) represents the intercept, (b) is the partial regression coefficient, (x) is the independent variable (in this case, rainfall), and (y) is the dependent variable, which is the time period for each projection.\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the climate graph of the five weather stations that were studied, with data ranging from 1948 to 2014, depending on the station.\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:\\varvec{y}=\\varvec{a}+\\varvec{b}\\varvec{x}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e3.3 Study of climate change\u003c/h3\u003e\n\u003cp\u003eThe study of climate change requires understanding its impact, vulnerability, and adaptation. For this reason, climate change scenarios were projected for the study area using publicly available data. These projections aimed to link current climate conditions with future changes in hydrological region 30. The climate models used for the projections were CNRMCM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR, focusing on two emission scenarios (RCP 4.5 and RCP 8.5) with forecasts for the years 2039, 2069, and 2099.\u003c/p\u003e\n\u003cp\u003eTo generate the projections, climatology data and anomalies were downloaded from the websites \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://atlasclimatico.unam.mx\u003c/span\u003e\u003c/span\u003e. Using the QGIS software (which is freely available), the data were adjusted to fit hydrological region 30, and specific scales were applied to track changes in climate variables. The final projections combined both the base climate data and the anomalies. These were calculated using the raster calculator function in QGIS, and the results were saved in an Excel file to create the tables and graphs shown in Fig.\u0026nbsp;6 in the results section.\u003c/p\u003e\n\u003ch3\u003e3.4 Homogeneity and trend analysis for gauging station \u0026ldquo;30019\u0026rdquo;\u003c/h3\u003e\n\u003cp\u003eThe statistical properties of hydrological data series, including the mean, standard deviation, and serial correlation coefficients, can be influenced by trends in the mean or variance, as well as by sudden positive or negative shifts. Such anomalies often result from a loss of homogeneity and inconsistencies within the series. Typically, these issues are caused by human activities like deforestation, agricultural expansion, river channel alterations, dam construction, and increased greenhouse gas emissions. Natural events, such as wildfires, earthquakes, landslides, and volcanic eruptions, can also trigger these anomalies.\u003c/p\u003e\n\u003cp\u003eTo assess the homogeneity of the data series, statistical tests are employed to propose a null hypothesis, providing a method for its acceptance or rejection. In this study, eight homogeneity tests were used to evaluate potential changes in the recorded data, aiming to identify the occurrence of climate change and pinpoint when such changes began. Specifically, the Pettitt, Standard Normal, and Buishand tests were utilized to identify the year in which these shifts started. Additionally, two trend detection tests were applied, while the Anderson test was used to assess the independence of the series (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec). These analyses were conducted at gauging station 30019 \"Boca del Cerro\" and five climate stations, with their identification numbers, names, and coordinates detailed in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(a) and 1(b).\u003c/p\u003e\n\u003cp\u003eTable 1 (a) Climatological Stations; (b) Gauging Station; (c) Tests applied to evaluate trend series at gauging station.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cstrong\u003ea)\u003c/strong\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabb\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eClimatological study station data for the Usumacinta River\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStation ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eName\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eUTM coordinates\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eAltitude\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eHydrological region\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eX (East)\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eY (North)\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePalizada, Palizada\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e595157.435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2012622.706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrijalva-Usumacinta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNueva Esperanza, Ocosingo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e752689.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1820276.686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrijalva-Usumacinta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoca del Cerro, (DGE)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e657524.785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1928184.483\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrijalva-Usumacinta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTenosique, Tenosique\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e666332.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1933788.253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrijalva-Usumacinta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTres Brazos-Tabasco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e541196.354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2032733.307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrijalva-Usumacinta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cstrong\u003eb)\u003c/strong\u003e\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tabc\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStation ID\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eName\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eUTM Coordinates\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAltitude\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRiver\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHydrological Region\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eX (East)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eY (North)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBoca del Cerro\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e681787.4789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1909122.954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUsumacinta River\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGrijalva-Usumacinta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTest\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHomogeneity tests\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePetit\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandard normal test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStatistician Buishand\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVon Newman test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFisher\u0026acute;s statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHelmert\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et-student\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCramer\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTrend tests\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMann Kendall\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpearman\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIndependence Test\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnderson Test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3.5 \u003cstrong\u003eFlood Study and Synthetic Flow Rates.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study of floods is conducted by analyzing areas identified as prone to flooding. In these places, flood models are applied using statistical studies of records from hydrometric or meteorological stations, which allows for the preparation of a descriptive report on the geological, climatological situation, vegetation, and human settlements in the area in question. These studies also cover natural spaces and provide a historical perspective.\u003c/p\u003e\n\u003cp\u003eCurrently, these studies are usually carried out by the private industry, as many of their projects are at risk of flooding, which can result in significant economic losses, especially in equipment such as compressors or filters. To a lesser extent, flood studies are conducted for communities to determine potential human losses. In the case of populations located along the Usumacinta River, these losses occur recurrently. Therefore, it is crucial to carry out studies of synthetic or projected flows for the future to mitigate these economic losses.\u003c/p\u003e\n\u003cp\u003eFor this study; recorded and synthetic flows were analyzed using the modified Svanidze method (Dom\u0026iacute;nguez-Mora., 2005). This method involves a two-step random procedure: first, the best-fit distribution function is used to generate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{m}\\)\u003c/span\u003e\u003c/span\u003e random values based on the distribution function of the total sum \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{n}\\)\u003c/span\u003e\u003c/span\u003e. Second, a random selection is made from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{m}\\)\u003c/span\u003e\u003c/span\u003e historical years to derive the synthetic percentages of annual totals for each series and their corresponding monthly fractions. Once the synthetic annual totals are obtained, each total is multiplied by the corresponding annual percentage and the monthly fractions for the randomly selected year, resulting in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{m}\\)\u003c/span\u003e\u003c/span\u003e synthetic periodic series (Dom\u0026iacute;nguez-Mora \u0026amp; Arganis-Ju\u0026aacute;rez., 2009) This procedure is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec.\u003c/p\u003e\n\u003cp\u003eThe study of the flood utilized data from gauging station 30019 \u0026ldquo;Boca del Cerro,\u0026rdquo; which has a 73-year record and is located 200 meters upstream of the southeast railway bridge in the town of Boca del Cerro, Tenosique Municipality, Tabasco. The hydrogram in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb was generated using the recorded maximum flow series shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea. River trace, direction, flow, and slope data were obtained using QGIS\u0026reg;, Google Hybrid\u0026reg;, and digital elevation models from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e. This information was then imported into HECRAS, where the flood study was conducted using its RasMapper tool to generate 1D and 2D flood maps.\u003c/p\u003e"},{"header":"4 RESULTS","content":"\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv class=\"mathdisplay\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ch3 id=\"FileID_Equd\" class=\"mathdisplay\"\u003e4.1 Availability of climatological and hydrometric data\u003c/h3\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003cp\u003eThe quality, quantity, and availability of climate information have been important for the development of research (Leal-Nares et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), climatological stations provide invaluable value in decision-making in the presence of natural phenomena such as hydrometeorological events, they allow to anticipate and record the behavior of meteorological phenomena such as floods.\u003c/p\u003e\n\u003cp\u003eIn this study, over 30 years of records were collected from five climatological stations\u0026mdash;4024, 7121, 27004, 27046, and 27050\u0026mdash;as well as from the gauging station 30019, according to the World Meteorological Organization (WMO, 2021). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the recorded series and their corresponding statistics. The average annual accumulated rainfall across the five climatological stations is 1866.68 mm. For station 4024, the accumulated rainfall (Hp) deviates from the mean by approximately 394.19 mm, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb. The greatest average deviation in rainfall is observed at station 7121, with 498.83 mm (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\n\u003cp\u003eGauging station 30019 records an average annual maximum flow (Q) of 5549.18 m\u0026sup3;/s, with a standard deviation of 2897.57. The recorded annual maximum flow for this station is 9153 m\u0026sup3;/s, while the minimum is 3566 m\u0026sup3;/s, as indicated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cstrong\u003e4.2 Average annual precipitation in Hydrological Region 30.\u003c/strong\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe study on the average annual precipitation in Hydrological Region 30 \"Grijalva-Usumacinta\" found that the region receives approximately 1,747,247 m\u0026sup3; of rainfall per year per km\u0026sup2;. According to data from the National Institute of Statistics and Geography (INEGI 2020), the region has a population of 6,557,760 and covers an area of 102,465 km\u0026sup2;. Based on this information, water availability was calculated to be 27,300.73 cubic meters per inhabitant per year, indicating a high level of water availability.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e4.3 Results of climate change study in Hydrological Region-30\u003c/h3\u003e\n\u003cp\u003eThe results of the climate change study, conducted using WorldClim and UNIATMOS, indicated an increase in precipitation compared to the baseline climate, as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The data in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea show an average rainfall of 310 mm, a maximum average temperature of 33\u0026deg;C, and a minimum average temperature of 4\u0026deg;C, which represent the baseline climate for the area. Climate change projections were made using the following models: CNRM-CM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR. The GFDL-CM3 model for RCP 4.5, with a time horizon from 2045 to 2069, projected a 20 mm increase in precipitation compared to the baseline climate, while for RCP 8.5, the increase was 40 mm. These results are depicted in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec for their respective concentration pathways. Regarding maximum temperatures, the baseline climate shows an average maximum temperature of 32\u0026deg;C. The most unfavorable data were provided by the HADGEM2-ES model for the periods 2045\u0026ndash;2069 and 2075\u0026ndash;2099 under RCP 4.5, indicating temperatures as high as 37\u0026deg;C, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed, representing a 5\u0026deg;C increase in maximum temperature. This is a critical issue to consider, especially due to the potential temperature rise during April, May, and June, which are key months for rainfed agriculture. In this region, 52% of the population lives in rural areas (Government of Mexico., 2009) and depends on seasonal rainfall. If temperatures increase, higher evapotranspiration will occur, leading to reduced aquifer recharge and limited water availability for irrigation, ultimately harming the local economy, which relies on livestock farming, forage crops, fishing, and seasonal agriculture. It is particularly important to address the rise in maximum temperature due to the risks it poses. Data for this variable can be seen in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee for the corresponding RCPs.\u003c/p\u003e\n\u003cp\u003eThe baseline minimum temperature is 4\u0026deg;C on average for November, December, and January. Climate change models predict an increase in minimum temperatures of approximately 10\u0026deg;C, leading to a projected average minimum temperature of 14\u0026deg;C for these months, as shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eg. Overall, the climate change study indicates a slight increase in rainfall but a sharp rise in both maximum and minimum temperatures. This highlights the need to focus on climate resilience, enabling humans to adapt to climate variability. It also underscores the importance of implementing global public policies aimed at gradual decarbonization to mitigate the impacts humans have had on the planet through excessive consumption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Hydrological Region-30.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trend analysis results reveal a significant increase in rainfall records from the five climatological stations analyzed. The data from stations 7121, 27004, and 27046, as well as gauging station 30019, showed an upward trend, as illustrated in Fig.\u0026nbsp;6a. In contrast, the series from climatological station 4024 exhibited a decreasing trend, as shown in Fig.\u0026nbsp;6b, while the records from station 27050 remained stable, as seen in Fig.\u0026nbsp;6c.\u003c/p\u003e\n\u003cp\u003eBased on these findings, there is evidence of an increasing trend in precipitation in the area, highlighting noticeable changes in climate variability. This is supported by the results of the water availability study, which revealed a significantly high-water supply, exceeding the threshold of 10,000 cubic meters per person per year, as per the Falkenmark index. This high availability aligns with the results of the climate change study, which indicates a significant rise in rainfall. Consequently, this study underscores the importance of understanding future uncertainties, particularly the challenges humanity faces due to changes and fluctuations in precipitation patterns. This should raise awareness of the critical importance of water as a resource for human survival, and how its excess can lead to natural disasters such as floods, which result in the loss of human lives.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ch3\u003e4.5 Homogeneity and trend test results for gauging station 30019\u003c/h3\u003e\n\u003cp\u003eTo perform the homogeneity and trend analysis, a time series with a minimum of 30 consecutive years of records is required (WMO., 2017), which is met in this case, as 66 years of data have been recorded, as shown by the maximum annual flows in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea.\u003c/p\u003e\n\u003cp\u003eBased on this, homogeneity and trend tests were applied to gauging station 30019. The results indicated that it is a non-stationary series, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb. The Pettitt test revealed a break in the year 1971, while the Standard Normal and Buishand Statistical tests identified changes starting from 1964. For the trend tests, Mann-Kendall and Spearman were applied, with the latter showing a decreasing trend. However, it is important to note that this test was not deterministic, as the trend analysis indicates an overall increasing trend. Therefore, based on the studies conducted, it is inferred that there is a shift with an upward trend. The independence test confirmed that the series is completely independent.\u003c/p\u003e\n\u003cp\u003e4.6 \u003cstrong\u003eFlood study results with measured and predicted flow rates.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the flood study, using the recorded annual maximum flow, the Froude number averaged less than 1, indicating the river was in a subcritical regime with low velocities, high water levels, and minimal sediment transport, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea. The flood hydrograph for this study, recorded on October 24, 2008, at gauge station 30019, coincided with the peak flow of the series, leading to significant flooding along the Usumacinta River. This was corroborated by news reports on October 21 of that year (Rivero Zapata., 2008), as depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eh. During this time, the Usumacinta River flooded Tenosique, Tabasco, a locality with a population of 60,000 as recorded in 2008 (INEGI., 2023).\u003c/p\u003e\n\u003cp\u003eUsing the modified Svanidze method for flood forecasting, a maximum flow was identified among the top 100 random numbers generated in the synthetic series, exceeding the previously recorded flow. This resulted in an increase in the critical water level and a slight rise in the Froude number, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb. Based on these results, the maximum flow obtained from the synthetic series was 11,735.51 m\u0026sup3;/s, ranking 18th among the synthetic flows (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ei). By adding 18 years to the last year of recorded data, 2022, it is inferred that a similar flood event could occur in 2040. It is important to note that official flow records are only available up to 2014; however, a review of gauge station 30019\u0026rsquo;s facilities revealed data up to 2022. When comparing the results from 2015 to 2022 with the synthetic data generated by the modified Svanidze method, a 95% accuracy was observed.\u003c/p\u003e\n\u003cp\u003eThe flood study for the synthetic flow revealed slightly higher velocities and an increased critical water level for most sections of the river, with the Froude number slightly exceeding 1. This suggests that a flood more severe than the one in 2008 could occur. Figures\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed illustrate the critical water level along the Usumacinta River, with Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec showing the actual maximum recorded flow, and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed displaying the synthetic maximum flow.\u003c/p\u003e\n\u003cp\u003eThe actual velocity results are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ee, while the synthetic velocity results are in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ef. Notably, velocities obtained with the actual maximum recorded flow exceeded 12 m/s, as indicated by the red zone in the velocity map in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ee.\u003c/p\u003e\n\u003cp\u003eVelocity data is crucial for assessing potential damage in flood-prone areas using the Dorrigo diagram, which defines five velocity ranges, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eg. In this diagram, point A marks where vehicle instability begins, making it moderately safe for pedestrians to walk, with a maximum safe velocity of 2 m/s. Point B indicates walking is unsafe, and beyond point C, structural damage to lightweight constructions occurs (Cruz Garc\u0026iacute;a, 2019). If the velocities in the red-marked zones of Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ee and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ef exceed 12 m/s, as compared to the Dorrigo diagram, structural damage, vehicle loss, and, most critically, loss of human lives will occur, as water at this speed will penetrate localities along the Usumacinta River. This scenario unfolded in Tenosique, Tabasco, in October 2008. Therefore, it is crucial to remain vigilant due to climatic variability to mitigate losses of human life and property. Urban sprawl near the Usumacinta River has increased, as shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ee and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ef, further raising vulnerability to climate change.\u003c/p\u003e\n\u003cp\u003eAccording to the results of this study, water availability in the area is 27,300.73 cubic meters per inhabitant per year, indicating very high-water availability, as it exceeds the 10,000 cubic meters per inhabitant per year threshold set by the Falkenmark index for regions with abundant water resources. Consequently, the likelihood of future floods is high, highlighting the need for increased awareness of these meteorological phenomena.\u003c/p\u003e\n\u003cp\u003eIn the climate change study using the CNRMCM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR models under RCP 4.5 and RCP 8.5 scenarios, projections for 2039, 2069, and 2099 forecast a 20 mm increase in precipitation for August and September compared to baseline climate data. Regarding maximum temperatures, the models predicted a 5\u0026deg;C increase, with future maximum temperatures projected to reach 37\u0026deg;C, compared to the baseline of 32\u0026deg;C. This suggests greater evapotranspiration, which may alter precipitation patterns and contribute to biodiversity loss in the region, threatening plant, animal, and insect species that are crucial for maintaining ecological balance.\u003c/p\u003e\n\u003cp\u003ePrecipitation projections from climatological stations show an upward trend, indicating an increased risk of flooding in the area. Similarly, projections from the gauge station also reflect rising trends, leading to the conclusion that the region is flood-prone. Studies on water availability, climate change, and the increasing trends observed in climatological stations, as well as the homogeneity and trend analysis from the gauge station, indicate that significant changes have occurred since 1964. These findings suggest that climate variability has been impacting the area since that time, as evidenced by the shifts in the homogeneity study of the flow series. This is further supported by the floods that have occurred over the past 20 years, with the largest flood event taking place in October 2008. According to forecasts generated by the modified Svanidze model, such events may recur with greater intensity around 2040. Synthetic data suggest a flow 28% higher than the maximum recorded flow, which implies that future floods could cause 28% more damage than the October 2008 flood.\u003c/p\u003e\n\u003cp\u003eGiven this, it is crucial to prepare society for these extreme weather events and take proactive measures to mitigate their impacts.\u003c/p\u003e\n"},{"header":"5 DISCUSSION","content":"\u003ch2\u003e5.1 Comparison with previous studies\u003c/h2\u003e\n\u003cp\u003eThe results of this study indicate a significant increase in precipitation and, consequently, in the flow rates of the Usumacinta River, which could lead to a higher risk of flooding. These findings are consistent with previous research that has addressed similar issues in other regions. For example, Abram et al., (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that human-induced global warming has led to an increase in the frequency of extreme events, including intense rainfall and prolonged droughts. Their studies, focused on various regions worldwide, demonstrate a climate change pattern. Like in this study, Abram et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e. observed an increase in precipitation during the wettest months, suggesting a global trend toward a more extreme climate.\u003c/p\u003e\n\u003cp\u003eDeepak et al., (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a geospatial analysis of flood vulnerability in several river basins and found that areas with high climate variability present a higher risk of extreme hydrometeorological events. Similarly, our study projects a 28% increase in the maximum flow of the Usumacinta River by the year 2040.\u003c/p\u003e\n\u003cp\u003eAnother relevant study is that of S\u0026aacute;nchez-Cohen \u0026amp; D\u0026iacute;az-Padilla, (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), who analyzed climate variability in Mexico and its hydrological impacts. They found that changes in precipitation patterns have led to a higher frequency of floods and droughts in various regions of the country. This results, showing an increase in precipitation and maximum temperatures in the Usumacinta River region, corroborate S\u0026aacute;nchez-Cohen \u0026amp; D\u0026iacute;az-Padilla, (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) conclusions, highlighting the need for adaptation strategies to address climate change.\u003c/p\u003e\n\u003cp\u003eAdditionally, Petrich, (2018) research on the Usumacinta River emphasized the importance of integrated water resource management and the construction of resilient infrastructure to mitigate flood impacts. Our findings, projecting a significant increase in flood risk for the coming decades, reinforce the urgency of implementing Petrich, (2018) recommendations to strengthen the resilience of local communities to extreme climatic events.\u003c/p\u003e\n\u003cp\u003eIn summary, the results of this study are consistent with the existing literature and underscore the importance of continuing research and applying mitigation and adaptation measures to address the challenges of climate change. The increasing trends of precipitation and maximum temperatures in the Usumacinta River region is a local manifestation of a global phenomenon that requires coordinated and sustained action at both regional and global levels.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e5.2 Practical implications\u003c/h2\u003e\n\u003cp\u003eThe findings of this study have several important practical implications that must be considered to improve the resilience of communities and infrastructure in the Usumacinta River region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlood-resilient infrastructure:\u0026nbsp;\u003c/strong\u003ethe projection of a 28% increase in the maximum flow of the Usumacinta River by the year 2040 underscores the urgent need to strengthen and adapt existing infrastructure to withstand flood events. This includes the construction and enhancement of levees, dams, and drainage systems capable of handling higher flow rates and preventing catastrophic overflows. Additionally, it is crucial to incorporate green infrastructure designs, such as buffer zones and wetlands, that can absorb and slow down water flow during extreme events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrban planning and land use:\u0026nbsp;\u003c/strong\u003elocal and regional authorities must review and update land use plans to limit development in flood-prone areas. Implementing zoning policies that discourage construction in high-risk zones and promote development in less vulnerable areas is essential. Furthermore, urban planning should include clear and accessible evacuation routes and the creation of open spaces that can serve as water retention areas during flood events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEarly warning systems and community preparedness:\u0026nbsp;\u003c/strong\u003eestablishing robust and efficient early warning systems is fundamental to reducing the vulnerability of local communities. These systems should include real-time meteorological and hydrological monitoring technologies that can detect increases in river flow and issue alerts well in advance, allowing communities to take preventive measures. Community education and training programs on flood preparedness and response are equally crucial for enhancing population resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated water resource management:\u0026nbsp;\u003c/strong\u003ewater resource management in the Usumacinta River region must be integrated and coordinated among various governmental entities and local organizations. Implementing management strategies that consider both water availability and the need to reduce flood risk is necessary. This can include regulating water extraction, protecting watersheds, and restoring natural ecosystems that can act as flood buffers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEnvironmental policies and regulations\u003c/em\u003e Given that climate change is a key factor contributing to climate variability and increased flood risks, it is crucial that national and regional environmental policies and regulations focus on mitigating the effects of climate change. This includes reducing greenhouse gas emissions, promoting renewable energy, and implementing sustainable practices in agriculture and industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContinuous research and monitoring:\u0026nbsp;\u003c/strong\u003eongoing research and monitoring are essential for improving our understanding of climate patterns and their impact on the Usumacinta River. Investing in long-term studies and advanced monitoring technologies is necessary to collect accurate data that can inform management strategies and public policies. Additionally, collaboration with academic and research institutions can provide valuable insights and develop innovative solutions to address climate change challenges.\u003c/p\u003e\n\u003cp\u003eThe results of this study highlight the need for immediate and coordinated actions to address flood risks in the Usumacinta River region. Implementing these practical implications will not only contribute to the protection of communities and infrastructure but also promote sustainable and resilient development in the face of climate change impacts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e5.3 Study limitations\u003c/h2\u003e\n\u003cp\u003eDespite the significant findings of this study, several limitations must be recognized to provide a balanced view of the results and their implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependence on historical data:\u0026nbsp;\u003c/strong\u003ethe accuracy of our climate and flow projections largely depends on the historical data collected from meteorological and gauging stations. The quality and availability of these data can vary which may introduce uncertainties into our predictive models. Additionally, some historical data may be subject to measurement errors or lack of continuous records, which can affect the consistency of our conclusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate models and assumptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of climate models such as CNRMCM5, GFDL-CM3, HADGEM2-ES, and MP1-ESM-LR involves certain assumptions about future greenhouse gas emission scenarios (RCP 4.5 and RCP 8.5). These assumptions may not fully capture the complexity of climate interactions and local variabilities specific to the Usumacinta River. Therefore, precipitation and temperature projections should be interpreted with caution and considered as possible scenarios rather than definitive predictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial and temporal variability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethe spatial and temporal variability of extreme climatic events cannot always be accurately captured in a regional study. Differences in topography, land use, and local hydrological characteristics can influence the magnitude and frequency of floods, which may not be fully reflected in our models. This limitation can lead to an underestimation or overestimation of flood risks in certain areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in land use and human activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003echanges in land use, such as urbanization, deforestation, and agricultural practices, can significantly impact river dynamics and flood patterns. Our study does not exhaustively address how these anthropogenic changes may interact with climatic factors to influence flood risks. Future research should consider these factors to obtain a more comprehensive understanding of the risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScalability of results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewhile this study focuses on the Usumacinta River region, the scalability of the results to other river basins may be limited due to differences in climatic, geographic, and socioeconomic conditions. The proposed adaptation and mitigation strategies must be tailored to the specific characteristics of each region to be effective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLack of consideration of socioeconomic impacts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ealthough this study focuses on climate and flow projections, it does not thoroughly address the socioeconomic impact of floods on local communities. Evaluating social and economic vulnerability, as well as post-disaster recovery strategies, are crucial aspects that should be considered in future research to develop a comprehensive response to flood risks.\u003c/p\u003e\n\u003cp\u003eAlthough this study provides valuable information on future climate and flow patterns in the Usumacinta River region, it is essential to consider these limitations when interpreting the results. Acknowledging these limitations not only improves the study's transparency but also guides future research toward areas needing more detailed and robust analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e5.4 Future research directions\u003c/h2\u003e\n\u003cp\u003eThe findings of this study open various opportunities for future research that can complement and expand our understanding of flood risks in the Usumacinta River region. Some key areas for future research include:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIntegration of land use changes\u003c/em\u003e Given that human activities, such as urbanization and deforestation, significantly impact the basin's hydrology, it is crucial to investigate how these changes affect precipitation and flow patterns. Future studies should develop models that integrate land use data and its evolution, allowing for more accurate flood predictions and more effective land use planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of socioeconomic vulnerability:\u0026nbsp;\u003c/strong\u003eit is essential to conduct studies that assess the socioeconomic vulnerability of local communities to floods. Research analyzing factors such as community response capacity, critical infrastructure, and social resilience mechanisms can provide a more comprehensive view of flood impacts and help design more effective adaptation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContinuous and enhanced hydrological data monitoring:\u0026nbsp;\u003c/strong\u003eimplementing advanced and continuous hydrological monitoring systems in the Usumacinta River basin is a priority. Future research should focus on developing and deploying sensor technologies and monitoring networks that can provide real-time data on flows, water levels, and precipitation. These data will improve predictive model accuracy and early responses to extreme events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of climate change on biodiversity:\u0026nbsp;\u003c/strong\u003eGiven the crucial role of the Usumacinta River in regional biodiversity, it is important to investigate how climate change and flood events affect aquatic and terrestrial ecosystems. Studies evaluating the impact on key species, ecological connectivity, and ecosystem services can help develop conservation and restoration strategies that mitigate the negative effects of climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of multivariable predictive models:\u0026nbsp;\u003c/strong\u003efuture research should explore the development of predictive models that incorporate multiple variables, such as precipitation, temperature, land use, and topographical features. These models can improve the accuracy of flood predictions and allow for the simulation of different climate change scenarios and their impact on the Usumacinta River basin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterregional comparative studies:\u0026nbsp;\u003c/strong\u003einterregional comparative studies should be conducted to compare findings from the Usumacinta River region with other river basins at national and international levels, as this can provide valuable insights into similarities and differences in flood patterns and their causes. These comparative studies can help identify best practices and management strategies that can be adapted to different contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffectiveness of adaptation strategies:\u0026nbsp;\u003c/strong\u003eevaluating the effectiveness of current and future adaptation strategies is crucial to ensuring the resilience of local communities. Research analyzing the implementation and impact of measures such as resilient infrastructure construction, reforestation, and land use policies can offer evidence-based recommendations to improve flood risk planning and management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity participation and perception:\u0026nbsp;\u003c/strong\u003ethe participation of local communities in flood risk planning and management is fundamental. Future research should explore community perceptions of climate change and floods, as well as their willingness to participate in adaptation initiatives. This can help design more effective education and awareness programs that promote collaboration and community empowerment.\u003c/p\u003e\n\u003cp\u003eThese future research directions are essential to advancing our understanding and management capabilities of flood risks in the Usumacinta River region. Interdisciplinary collaboration and a focus on practical, evidence-based solutions are crucial for addressing climate change challenges and protecting both human communities and natural ecosystems.\u003c/p\u003e\n"},{"header":"6 CONCLUSIONS","content":"\u003cp\u003eThis study has provided an in-depth analysis of climate variability in the Usumacinta River basin, underscoring the significant impact of climate change on precipitation and more flow patterns. The key findings and their implications are summarized as follows:\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eIncrease in precipitation and more flows\u003c/strong\u003e: A rising trend in precipitation has been observed at the five climatological stations analyzed, along with an increase in Usumacinta River flows since 1964. Climate change models project up to a 40 mm rise in precipitation and a 5\u0026deg;C increase in maximum temperatures by the mid-21st century, significantly heightening the region's flood risk.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eFlood risk\u003c/strong\u003e: Projections using the modified Svanidze method suggest that maximum annual flows could increase by 28% by 2040. This potential rise emphasizes the urgent need to implement adaptation measures to protect local communities and critical infrastructure from future flood events.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eImplications for water resource management\u003c/strong\u003e: While high water availability in the region benefits certain economic activities, it also poses challenges for flood risk management. Developing an integrated approach to water resource management is crucial, balancing flood mitigation strategies with long-term sustainability goals.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eNeed for resilient infrastructure\u003c/strong\u003e: To reduce the impact of future floods, investment in resilient infrastructure is essential. This includes improving levees, drainage systems, and creating natural buffer zones. Urban planning should also prioritize zoning policies that restrict development in flood-prone areas.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eEnhancing community resilience\u003c/strong\u003e: Strengthening early warning systems, community education programs, and evacuation plans is vital for improving local resilience to floods. Engaging communities in disaster preparedness and response planning can significantly enhance their capacity for recovery after flood events.\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003ePublic policies and climate change\u003c/strong\u003e: Developing and implementing public policies that address climate change is critical to reducing the region's vulnerability. This includes promoting sustainable practices, cutting greenhouse gas emissions, and protecting natural ecosystems that serve as natural flood barriers.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study highlights the need for a coordinated, multidisciplinary approach to addressing the challenges posed by climate change in the Usumacinta River basin. Immediate and strategic actions are required to enhance resilience to future floods, thereby safeguarding both communities and the environment. The findings also highlight several areas that warrant further research, such as the integration of land-use changes, socioeconomic vulnerability assessments, continuous hydrological and climate monitoring, and the development of multivariate predictive models. These efforts will provide a more comprehensive understanding of flood risks and enable more effective management in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eACKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eAcknowledgments to the National Autonomous University of Mexico, CONAHCYT, and the Institute of Engineering for the support provided to carry out this research.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY STATEMENT\u003c/p\u003e\n\u003cp\u003eData can be made publicly available.\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTEREST\u003c/p\u003e\n\u003cp\u003eThe authors declare there is no conflict.\u003c/p\u003e\n\u003cp\u003eDECLARATION OF GENERATIVE AI IN SCIENTIFIC WRITING\u003c/p\u003e\n\u003cp\u003eStatement: During the preparation of this work the author(s) used Academic GPT in order to review the writing process. 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The updated \u003cem\u003e30-year reference period reflects the changing climate\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://public.wmo.int/en/media/news/updated-30-year-reference-period-reflects-changing-climate\u003c/span\u003e\u003cspan address=\"https://public.wmo.int/en/media/news/updated-30-year-reference-period-reflects-changing-climate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Precipitation, Svanidze, Forecast flows, flood","lastPublishedDoi":"10.21203/rs.3.rs-6579639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6579639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis case study explores the impact of climate variability and change on the Usumacinta River, the most water-abundant river in Mexico. Climate change has intensified extreme hydrometeorological events, increasing the vulnerability of communities along the river. Historical climate data from meteorological stations, along with models (CNRMCM5, GFDL-CM3, HADGEM2-ES), were used to project future climate scenarios under RCP 4.5 and RCP 8.5. Results show a predicted increase in precipitation and temperature by 2040, with maximum temperatures rising by up to 5°C. Using the modified Svanidze method, the study forecasts a 28% increase in river flow compared to the highest recorded flood in 2008, posing significant risks to local populations and infrastructure. The findings highlight the need for resilient infrastructure, improved water management, and climate adaptation strategies to mitigate future flood risks. The Usumacinta River plays a crucial role in regional biodiversity and economic activities, making it essential to prioritize flood prevention and disaster preparedness. This research provides critical insights for strengthening the resilience of communities in the face of growing climate threats.\u003c/p\u003e","manuscriptTitle":"Hydroclimatic Risk Analysis in the Usumacinta River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:30:24","doi":"10.21203/rs.3.rs-6579639/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81725571-f471-457a-942a-712f1e313eb0","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-19T07:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:30:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6579639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6579639","identity":"rs-6579639","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}